Is Your Workforce AI Pilot Ready to Scale?
Actionable guidance for IT and digital workspace leaders to move beyond AI pilots and toward organization-wide impact.
Workforce AI pilots — projects initiated to empower employee productivity through AI technologies — are popping up in every corner of the enterprise, from meeting summaries to workflow automation. Yet, most stall out. A recent S&P Global report found that only 27% of enterprises using generative AI have achieved organization-wide adoption. That gap represents not just lost ROI, but lost ground to competitors who are moving faster, innovating quicker and delivering better employee experiences.
The question isn't whether to invest in AI. It's whether your pilots are ready to scale and deliver measurable business value before others outpace you.
WWT and Softchoice were among the earliest adopters of workforce AI — testing, learning and refining how to drive real impact across the enterprise. Through those internal initiatives, we've learned what works and what doesn't. We've also partnered with clients across industries, hosted candid conversations on the AI Proving Ground podcast and published WWT Research to help uncover what separates stalled pilots from scaled success.
We've compiled these insights and lessons learned to help you assess your organization's readiness, prioritize use cases and accelerate the path from pilot to ROI.
The risks of staying in pilot mode
In our Digital Workspace Priorities research report, we discuss the criticality of shifting from experimenting with AI to scaling it across the workforce to drive real business outcomes.
When AI remains confined to isolated pilots, the risks start to compound:
The organizations that successfully scale workforce AI will empower their people, unlock new efficiencies and build a foundation for continuous innovation.
Determining your readiness
Before scaling, pressure-test whether your pilots can withstand a broader rollout. Ask yourself five quick questions:
- Are pilots tied to KPIs we already track (e.g., resolution times, SLA compliance, employee experience)?
- Have we started with high-volume, repetitive workflows where ROI is easiest to prove?
- Do we have a visible VP or senior leader clearing roadblocks and championing adoption?
- Beyond training, do we have champions and peer learning in place to drive adoption?
- Is the right data tagged, secured and governed for responsible scale?
If you can't answer "yes" across the board, you're not alone. Most organizations are still closing these gaps. The key is identifying where you stand and building a plan to move forward.
Moving from pilot to enterprise-wide scale
Think of scaling GenAI the way you would onboard a new employee. It needs a clear job description, a manager who champions it, the right access to systems and data, and ongoing feedback to improve performance. Framing AI this way makes scaling more tangible.
Write AI's job description
When you hire a new employee, you don't give them every responsibility on day one. You start with a focused role that delivers measurable value to the team. AI should be treated the same way. Rather than launching ambitious or flashy pilots, assign AI to workflows where impact is easy to prove and directly connected to metrics your organization already tracks (more on this later).
Some of the most impactful ways that AI is redefining work are through:
By starting with well-defined "job responsibilities" that tie back to business outcomes, you'll build credibility and momentum for scaling.
Assign AI a manager
Even the best employees need a strong manager to clear roadblocks and advocate for their success. AI is no different. Scaling requires a visible advocate at the VP level or above who can legitimize adoption across teams and ensure resources are available.
As a director or senior manager, your role is to effectively engage that ally and show them why the initiative matters. Frame use cases in terms your sponsor values — like faster resolution times, onboarding speed or workflow throughput. Share early wins so they can confidently champion expansion, and keep them informed with updates and success stories so they can advocate internally.
- Craig McQueen, Softchoice VP for AI Solutions
Onboard and mentor AI
Like a new employee, AI requires onboarding, mentoring and reassurance to reach full productivity. Leaders should identify AI champions who can mentor peers, share best practices and normalize new ways of working. Formal training should be paired with interactive, hands-on sessions that make AI feel approachable.
You'll also want to address fears of becoming obsolete (FOBO) by clarifying how AI augments human roles and backing this up with reskilling investments. Celebrate your early adopters and highlight measurable improvements to encourage others to adopt too.
Give AI the right tools and permissions
No employee can succeed without access to the right systems, tools and data. The same is true for AI. Scaling workforce AI means scaling access to data, which requires tagging, access controls and governance structures that ensure both compliance and trust.
Directors and managers play a key role in bridging IT, security and business teams to prepare the environment. Establishing a Center of Excellence or steering committee can help align priorities, manage risk and keep adoption on track.
By embedding governance early, you'll enable AI to "do its job" effectively without introducing compliance or security gaps.
Review AI's performance
Just like you measure an employee's performance based on their impact on business outcomes, you must measure AI's value in operational terms. Time saved or pilot completion metrics are a start, but not enough to justify scale. Instead, track improvements in KPIs your business already values.
For example, in customer service, AI-assisted workflows can reduce resolution time, improving SLA compliance and customer satisfaction. In healthcare, faster access to information can boost patient throughput and free clinicians to spend more time on care. In sales or proposal teams, AI can generate higher-quality proposals that increase pipeline coverage and conversion rates.
By evaluating AI performance against real business outcomes, you provide clear evidence of value, which in turn fuels continued investment and expansion.
Next steps
We know scaling AI isn't easy. We've guided many organizations from pilot to enterprise-wide adoption and know firsthand the challenges that can surface along the way.
A prime example is our work with a leading healthcare provider that scaled GenAI-powered search and automation from 4,800 to 24,000 users, empowering clinicians to access the information they need faster and focus more on patient care.
We can help your organization achieve the same. WWT and Softchoice empower organizations to define, build and scale AI solutions through a unified, outcome-driven approach. From strategic consulting and use case identification to infrastructure integration and global deployment, we streamline every phase of the AI lifecycle. With our deep technology partnerships, validation environments and supply chain capabilities, we make sure your AI initiatives are continuously optimized for performance and growth.
Request our Workforce AI briefing today to learn how we can help you scale with confidence. And if you want to hear more stories about how other leaders are making it happen, tune in to these AI Proving Ground podcast episodes:
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This report is compiled from surveys WWT Research conducts with clients and internal experts; conversations and engagements with current and prospective clients, partners and original equipment manufacturers (OEMs); and knowledge acquired through lab work in the Advanced Technology Center and real-world client project experience. WWT provides this report "AS-IS" and disclaims all warranties as to the accuracy, completeness or adequacy of the information.